Method to reduce data rates and power consumption using device based attitude generation

ABSTRACT

A method includes generating motion data by receiving a gyroscope data from a gyroscope sensor, performing integration using the gyroscope data and generating an integrated gyroscope data using a first processor. The method further includes receiving a data from one or more sensors, other than the gyroscope sensor, and performing sensor fusion using the integrated gyroscope data and the data to generate motion data using a second processor.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 13/965,131, filed on Aug. 12, 2013, titled “Methodto Reduce Data Rates and Power Consumption Using Device Based AttitudeGeneration”, by Keal et al., which claims priority to U.S. ProvisionalApplication No. 61/786,414 filed on Mar. 15, 2013, titled “Method toReduce Data Rates and Power Consumption Using Device Based AxisQuaternion Generation”, by Keal et al., the disclosures of which areincorporated herein by reference as though set forth in full.

BACKGROUND

Various embodiment of the invention relate generally to motion sensordata and particularly to integration of gyroscope data to generatemotion data.

In applications using gyroscopes, gyroscope data from a gyroscope iscollected along with data from a compass and/or an accelerometer andother sensors. The collected data is then fused together to determinethe movement of the device. The collected data is also integrated overtime and at a high rate to reduce errors. Such high rate data istypically sent from a semiconductor device (or integrated circuit) to aprocessor. Due to the high rate associated with the integrated data,transmitting high rate data to the processor causes increased power.There is therefore a need for efficient transmission of gyroscope datawith a decrease in system power.

SUMMARY

Briefly, a method of the invention includes generating motion data byreceiving a gyroscope data from a gyroscope, performing integrationusing the gyroscope data and generating an integrated gyroscope datausing a first processor. The method further includes receiving a datafrom one or more sensors, other than the gyroscope, and performingsensor fusion using the integrated gyroscope data and the data togenerate motion data using a second processor.

A further understanding of the nature and the advantages of particularembodiments disclosed herein may be realized by reference of theremaining portions of the specification and the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows lower rate quaternion data being passed from device 14 todevice 10, in accordance with an embodiment of the invention.

FIG. 2 shows a block diagram of the relevant steps and/or data paths, ofthe embodiment of FIG. 1.

FIG. 3a shows a comparison of the integration of data by a conventionalgyroscope, and the various embodiments of the invention.

FIG. 3b shows some of the relevant steps of the processing of gyroscopeinformation, in accordance with a method of the invention.

FIGS. 4a-c show conventional processing of raw gyroscope data at 400 incomparison with that of the various embodiments of the invention at 402and 404.

FIGS. 5a-d show various embodiments of the invention, 502, 504, 506, and508, with each having MPU 14.

FIGS. 6 through 12 show various embodiments 600 through 1200 of theinvention, respectively.

FIG. 13 shows an embodiment in accordance with the invention.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following description of the embodiments, reference is made tothe accompanying drawings that form a part hereof, and in which is shownby way of illustration of the specific embodiments in which theinvention may be practiced. It is to be understood that otherembodiments may be utilized because structural changes may be madewithout departing from the scope of the present invention. It should benoted that the figures discussed herein are not drawn to scale andthicknesses of lines are not indicative of actual sizes.

In accordance with an embodiment and method of the invention,integration of gyroscope data is performed in a single device, such as asingle semiconductor device. Gyroscope integration is performed at arate that is higher than the integrated output data rate to reduce thein-device processing requirements. In an embodiment of the invention,integration of the gyroscope data is performed using a curve-fit toreduce the in-device processing requirements. Integration over time, ata higher rate, causes fewer errors. Yet, the integrated data is sent ata lower data rate thereby reducing power consumption. The integratedgyroscope data can also be passed at the same rate as the gyroscope datato reduce computation on the second processor and maintain a consistencyfor the type of data being sent. In the described embodiments, theintegrated gyroscope data is also referred to as a low power quaternion(LPQ).

In other embodiments and methods of the invention, sensor fusion isperformed at a lower rate than the rate of the gyroscope dataintegration. The output of a gyroscope-only quaternion data is saved ina register. In another embodiment, the quaternion data is stored in afirst-in-first-out (FIFO) that resides in the same device that includesthe gyro. In the case of a semiconductor, the “same device” as usedherein refers to the same chip. In some embodiments, the integration ofa three-axis gyroscope in the same package as the gyroscope sensor.

In yet another embodiment and method of the invention, removal of aknown gyroscope bias is performed after integration. In yet otherembodiments the gyroscope bias is removed before integration.

In yet other embodiments, a 9-axis (gyroscope, accelerometer, andcompass) motion tracking is employed in a variety of sensor platformarchitectures, some of which are disclosed herein, and the remainder ofwhich are too numerous to list but that are contemplated.

In the described embodiments, a motion tracking device also referred toas Motion Processing Unit (MPU) includes at least one sensor in additionto electronic circuits. The sensors, such as the gyroscope, the compass,the accelerometer, microphone, pressure sensors, proximity, ambientlight sensor, among others known in the art, are contemplated. Someembodiments include accelerometer, gyroscope, and magnetometer, whicheach provide a measurement along three axis that are orthogonal relativeto each other referred to as a 9-axis device. Other embodiments may notinclude all the sensors or may provide measurements along one or moreaxis. The sensors are formed on a first substrate. Other embodiments mayinclude solid-state sensors or any other type of sensors. The electroniccircuit in the motion tracking device receive measurement outputs fromthe one or more sensors. In some embodiments, the electronic circuitprocesses the sensor data. The electronic circuit is implemented on asecond silicon substrate. The first substrate is vertically stacked andattached to the second substrate in a single device.

In an embodiment of the invention, the first substrate is attached tothe second substrate through wafer bonding, as described in commonlyowned U.S. Pat. No. 7,104,129 (incorporated herein by reference) thatsimultaneously provides electrical connections and hermetically sealsthe MEMS devices. This fabrication technique advantageously enablestechnology that allows for the design and manufacture of highperformance, multi-axis, inertial sensors in a very small and economicalpackage. Integration at the wafer-level minimizes parasiticcapacitances, allowing for improved signal-to-noise relative to adiscrete solution. Such integration at the wafer-level also enables theincorporation of a rich feature set which minimizes the need forexternal amplification.

In the described embodiments, raw data refers to measurement outputsfrom the sensors which are not yet processed. Motion data refers toprocessed raw data. Processing may include calibration to remove erroror applying a sensor fusion algorithm or applying any other algorithm.In the case of the sensor fusion algorithm, data from one or moresensors are combined to provide an orientation of the device. In thedescribed embodiments, a motion processing unit (MPU) may includeprocessors, memory, control logic and sensors among structures.

Referring now to FIG. 1, device 10 is shown, in accordance with anembodiment of the invention. Device 10 is shown to include a processor12 and a motion processing unit (MPU) 14. Processor 12 residesexternally to MPU 14 and can be a general purpose processor or aspecialized processor. In some embodiments, processor 12 is referred toas Application processor (AP). MPU 14 may include a 2 or 3 axisgyroscope or a 3-axis accelerometer or a 6-axis sensor, i.e. 3-axisaccelerometer and 3-axis gyroscope, or a 9-axis sensor, i.e. 3-axisaccelerometer, 3-axis gyroscope, and 3-axis magnetometer (or compass).In another embodiment, MPU 14 may include a multitude of 3-axisaccelerometers, where the accelerometers provide the 3-axes of gyroscopedata by determining 3-degrees of freedom of angular velocity. MPU 14 mayinclude a digital motion processor (DMP) 3, such as a specializedprocessor, memory, analog-to-digital converter (ADC), or a controller.

In operation, MPU 14 generally processes gyroscope data 9 (“gyroscopemeasurement output” or “gyroscope data”) transmitted by gyroscope 18 inDMP 3 and outputs the orientation of the gyroscope in the form of theintegration gyroscope data 9 a. MPU 14 may transmit data 7 from othersensors, such as but not limited to an accelerometer and/or a compass.MPU 14 generates a gyroscope data output 5 to processor 12. Data 5 has adata rate that is lower than the rate of the gyroscope data output 9. Apart of the processing performed by the MPU 14 is quaternion and/orintegration in DMP 3.

Similarly, MPU 14 receives raw motion data 11 from accelerometer 20 andthe compass 22 and stores raw motion data 11 in FIFO 24 beforetransmitting to the processor 12.

In FIG. 1, processor 12 is shown to include a sensor fusion thatreceives the data output from the MPU 14 and generates motion data.

In some embodiments, processor 12 and MPU 14 are formed on differentsubstrates and in other embodiments they reside on the same substrate.In yet other embodiments, the sensor fusion is performed externally tothe processor 12 and MPU 14. In still other embodiments, the sensorfusion is performed by MPU 14.

Referring still to FIG. 1, MPU 14 is shown to include a high-rate andlow-power Quaternion block 16. Block 16 is shown to receive input fromthe gyroscope 18 and processes the same using processor 3 (and hardwarelogic) and outputs Quaternion 9 a. The output of gyroscope 18 isactually the output of the gyroscope. Raw data from compass 22 andaccelerometer 20 along with the quaternion output 9 a are transmitted tothe processor. In some embodiments, quaternion, performed by block 16,is integration. In this respect, block 16 includes an integrator thatintegrates the output of gyroscope 18 at a high rate. In someembodiments, this high rate is at a rate higher than 200 Hz. The block16, upon performing integration, provides the output of the integration(or Quaternion) to a bus, memory and register 26.

Register 26 and FIFO 24 each provide a low rate output to processor 12.Processor 12 further performs processing by performing a 6 or 9-axissensor fusion calculation based upon the integrated 3-axis gyroscopedata 5 and accelerometer and compass data 7. Data integration at highrate at the block 16 advantageously increases data reliability whereasthe low rate between MPU 14 and processor 12 advantageously reducespower consumption. It is noteworthy that block 16, gyroscope 18, compass22 and accelerometer 20 reside on the same package, in an embodiment ofthe invention and reside on the same substrate in another embodiment ofthe invention.

Separation of the MPU 14 and the processor 12 allows users of the MPU 14to flexibly use the output of the MPU 14. For example, different usersof the device 10 may choose the flexibility to use various sensor fusionalgorithms while still lowering the power from a reduced transmissionrate.

Integration may be performed using sampling that starts from thegyroscope measurement output. In other embodiments of the invention, thesampling starts after a predetermined number of samples and continuesover a predetermined time period. In yet other embodiments of theinvention, during a dead zone where the input of the integrator orQuaternion (gyroscope data) is close to zero, the output is made toremain the same.

FIG. 2 shows a block diagram of the relevant steps and/or data paths, ofthe embodiment of FIG. 1. In FIG. 2, gyroscope data, in its raw form, at200 is provided to the integration and quaternion block 206, which is apart of the block 16 in FIG. 1. Further details of the Quaternion areprovided later below. The integration at 206, as indicated above, isperformed at a high rate and the output of 206 is provided to theFIFO/Register 24 for storage. The output of FIFO/register 24 is aQuaternion that is provided to the sensor fusion 208. In an embodimentof the invention, sensor fusion 208 is part of application processor(AP) 12. In another embodiment of the invention, sensor fusion 208 ispart of MPU 14.

FIFO/Register 24 is also provided with a requested data rate at 210 andthe requested data rate at 210 uses one of the outputs of the sensorfusion 208 to determine the data rate being requested before passingalong this information to FIFO/Register 24. Another output of sensorfusion 208 is shown to be the motion data at 216.

Sensor fusion 208 is shown to further receive measurement outputs fromaccelerometer data 202 and compass data 204. If all three types of data,i.e. gyroscope data, accelerometer data, and compass data, are employedby the sensor fusion 208, the output 216 is a 9-axis Quaternion. Inanother embodiment, sensor fusion 208 may receive measurement outputsfrom other sensors such as pressure sensor or microphone. In someembodiments, sensor fusion block 208 may be part of MPU 14 or part of AP12.

In some embodiments, a bias removal block 214 for removing an inherentbias in the gyroscope. For example, after the gyroscope data at 200 isprovided, bias removal 214 can use used to remove the gyroscope'sinherent bias. Alternately, bias removal 214 can be applied afterintegrating gyroscope data in the sensor fusion 208. As will be evidentshortly, bias removal may be employed at stages other than at the outputat 200.

FIG. 3a shows a comparison of the integration of data by a conventionalmethod, at 300, and the various embodiments of the invention, at 302.FIG. 3b shows some of the relevant steps of the processing of gyroscopeinformation, in accordance with a method of the invention.

Referring now to FIG. 3a , in conventional method 300, raw data fromgyroscope is transmitted at a low data rate to a host processor for dataintegration. Precision of gyroscope data can be lost in this process andtransmitting data at higher rates consumes more power. In the variousembodiments of the invention, at 302, raw gyroscope data is integratedat a higher rate than done in the convention method due to theavailability of processing power. Integrated data is then transmitted atslower speeds to the host processor for further processing by the latterwhile retaining the precision of gyroscope data as well consuming lowerpower. Graph 304 shows the precision loss of prior art integrations ascompared to the higher precision shown in the graph at 306.

FIGS. 4a-c show conventional processing of raw gyroscope data at 400 incomparison with that of the various embodiments of the invention at 402and 404. In 400, the gyroscope data rate and the sensor fusion rate canbe the same. At 402, the sample rate is the same as shown at 400, exceptthe read rate at 402 is equal to a lower rate, such as the screenrefresh rate because the data is buffered. At 404, the sample rate isequal to the screen refresh rate ex. 60 Hz. By transmitting the 3-axislow power quaternion (LPQ) data at a rate lower than what is shown at400, power is saved. The 3-axis refers to 3-axis of the gyro beingintegrated into a quaternion. 402 has a disadvantage as there is moredata traffic than 404. A watermark in FIG. 4 refers to sending aninterrupt every at a predetermined number of samples rather than atevery sample.

FIGS. 5a, 5b, 5c and 5d show various embodiments of the invention, 502,504, 506, and 508, with each having MPU 14 for receiving measurementoutputs from sensors, and application processor 10 (AP) receiving itsinput in alternative ways from alternative sources via a bus. Optionallyexternal sensor may provide measurement data from various externalsensors 520 such as pressure, microphone, proximity, and Ambient Lightsensor. In FIG. 5a , in the first embodiment 502, MPU 14 receivesmeasurement output from the sensors, executes the low power quaternionon the raw gyroscope data and transmits sensor data to the AP 10 at arate lower than the gyroscope data via a bus where sensor fusionalgorithm in executed. Optionally, sensor fusion algorithm maybeexecuted on the data from MPU 14 as well as from external sensors 520.In an embodiment, the bus can be an I²C bus or any other bus.

In FIG. 5b , in the second embodiment 504, AP 10 receives its input,through the bus, from the Micro Controller Unit (MCU) 522. In anembodiment, MCU 22 includes a processor and memory. In this embodiment,MPU 14 executes the Low Power Quaternion algorithm where it integratesthe gyroscope data into a quaternion and the MCU 522 executes the sensorfusion algorithm. MCU 522 transmits processed motion data to the AP 10at a lower rate.

In FIG. 5c , in another embodiment 506, MPU 14 transmits raw sensor dataa higher rate to the MCU 522. MCU 522 executes the low power quaternionalgorithm where it integrates the gyroscope data into a quaternion. MCU522 transmits MPU 14 data to AP 10 at a lower rate optionally along withthe raw data from the external sensors. In this embodiment, the sensorfusion algorithm is executed in AP 10.

In FIG. 5d , in another embodiment 508, MPU 14 executes the low powerquaternion algorithm on the gyroscope data where it integrates thegyroscope data and transmits data to MCU 522. MCU 522 transmits data toAP 10 where sensor fusion algorithm is executes. Optionally, MCU 522transmits data from external sensors 520.

FIGS. 6 through 12 show various embodiments 600 through 1200,respectively, of the invention. More specifically, these figures are ofvarious configurations of a device. It is understood that theseconfigurations are merely exemplary and that others are contemplated.FIG. 6 shows a device 600 to include an AP 602 that is in communicationwith a MPU 604, which includes an accelerometer and a gyro. The MPU 604generates integrated gyro data that is sent to 602. In AP 602 the gyrobias is determined in 609 using the integrated gyro data. The gyro biascan be determined by using gyro data derived by taking the derivative ofintegrated gyro data or by use of a Kalman filter. The gyro bias isremoved from the gyro integrated data for use in the 6-axis sensorfusion.

FIG. 7 shows a MPU 700 is shown to include a gyroscope, accelerometer,and a compass and generates a 9-axis motion data as well as a 6-axismotion data.

FIG. 8 shows another MPU 800 to include a gyroscope, accelerometer and acompass feeding into a sensor fusion, which outputs the motion data. Theoutput of the gyroscope is integrated and bias correction is performed.

FIG. 9 shows a device 900 to include an AP that is in communication witha MPU through a MCU. Bias correction is performed by the AP and providedto the gyroscope, accelerometer, and compass of the MPU. The AP is shownto output motion data. In this embodiment, MCU functions as a feedthrough device.

FIG. 10 shows a device 1000 including an AP 10 to be coupled to a MCU522, which is coupled to the MPU 14. Bias correction/removal isperformed by the MCU 522 and the result provided to the MPU 14. The MPUincludes a gyroscope, an accelerometer, and a compass.

FIG. 11a shows a device 1100 a to include an AP that is coupled to aMCU. The MCU is shown coupled to the MPU. The MPU in addition to agyroscope, accelerometer, and a compass, includes a LPQ whose output isused for bias removal by the MCU. The MCU has a block which takes a LPQwith a bias on it coupled to a bias correction block to remove the biasbefore sending the LPQ to the sensor fusion 6-axis block.

FIG. 11b shows a device 1100 b that is analogous to the embodiment ofFIG. 11a except that the LPQ of the MCU of the device 1100 b is notcoupled to the bias correction block. Bias is provided by the MCU, butthe correction takes place in the MPU.

FIG. 12 shows a device 1200 to include an AP that is coupled to an MCU.The MCU is shown coupled to a MPU, which includes a gyroscope, anaccelerometer, and a compass. Rather than integrating the output of thegyroscope, a bias correction block and a LPQ, both in the MPU, arecoupled to each other and the bias correction block is coupled to thegyroscope. The LPQ generates an output to the MCU. The MPU generates a6-axis and 9-axis corrections.

The sensor fusion of the various embodiments of the invention calculatesthe quaternion in accordance with the algorithm and relationships below.

FIG. 13 shows an embodiment 1300 where the sensor data is generated at1301 and passed to processor1 1302. The sensor data includes gyroscopedata as well as other sensor data. Processor1 1302 integrates gyroscopedata into a quaternion and transmits the quaternion to anotherprocessor2 1303 where sensor fusion of the integrated gyroscope andother sensor data occurs. In an embodiment, sensor 1301, processor1 1302and processor2 1303 can be on the same semiconductor chip or on threedifferent semiconductor chips. In another embodiment, sensor 1301 andprocessor1 1302 can be on the same semiconductor chip. In anotherembodiment, sensor processor1 1302 and processor2 1303 are on the samesemiconductor chip.

A unit quaternion, also referred to as “quaternion”, is a 4-elementvector that describes how to go from one orientation to anotherorientation. Other equivalent ways to describe going from oneorientation to another orientation include a rotation matrix and Eulerangles. A unit quaternion has a scalar term and 3 imaginary terms and amagnitude of one for the four element vector. For the purpose ofdiscussion herein, the scalar term is placed first followed by theimaginary term. One skilled in the art would be able to compute slightlydifferent formulas using a different definition of a quaternion. Eulerangles describe how to go from one orientation to another orientation byrotating 3 different angles about 3 different axes of rotation. Insteadof moving 3 angles about 3 different axes of rotation, the samerotational motion can be described by rotating one angle around a singlevector. In Equation 1 below, for a quaternion, the angle θ is the amountrotated about the unit vector, [u_(x),u_(y),u_(z)].

$\begin{matrix}{\overset{\_}{Q} = \left\lbrack {{\cos \left( \frac{\theta}{2} \right)},{{\sin \left( \frac{\theta}{2} \right)} \cdot u_{x}},{{\sin \left( \frac{\theta}{2} \right)} \cdot u_{y}},{{\sin \left( \frac{\theta}{2} \right)} \cdot u_{z}}} \right\rbrack} & {{Equation}\mspace{14mu} 1}\end{matrix}$

A quaternion multiplication is defined in Equation 2 as found inliterature. The notation “⊗”, as used herein, represents quaternionmultiplication in this document.

Q ₁ =[q _(1w) , q _(1x) , q _(1y) , q _(1z)]

Q ₂ =[q _(2w) , q _(2x) , q _(2y) , q _(2z)]

Q ₁ ⊗ Q ₂ =[q _(1w) ·q _(2w) −q _(1x) ·q _(2x) −q _(1y) ·q _(2y) −q_(1z) ·q _(2z) , q _(1w) ·q _(2x) +q _(1x) ·q _(2w) +q _(1y) ·q _(2z) −q_(1z) ·q _(2y) , q _(1w) ·q _(2y) −q _(1x) ·q _(2z) +q _(1y) ·q _(2w) +q_(1z) ·q _(2x) , q _(1w) ·q _(2z) +q _(1x) ·q _(2y) −q _(1y) ·q _(2x) +q_(1z) ·q _(2w)]  Equation 2

A quaternion inverse is defined as:

Q ₁′=[q _(1w) , −q _(1x) , −q _(1y) , −q _(1z])  Equation 3

Sensor fusion will take measurements from various sensors, such as agyroscope, an accelerometer and a compass then combine the measurementsto determine the orientation of a device referenced to a fixed frame.Each sensor has good points and bad points when combining the data. Arate gyroscope (gyroscope hereafter) is good indicating how theorientation changes overtime without being affected by magneticdisturbance or acceleration. However, a gyroscope often has an offsetwhich leads to angular drift over time. There are many methods ofimplementing the sensor fusion, but one common aspect of many of thoseis that the gyroscope data must be integrated. If a gyroscope sensor wasperfect and integration was perfect, there would be no need for sensorfusion. However, sensors are not perfect and integration is not perfect,so other sensors are used to correct the orientation. One of the errorsincurred, is integration of the gyroscope data. To lower the integrationerror, the gyroscope data is often integrated at a high rate. Typicallya sensor fusion algorithm will take raw sensor data and compute anorientation. The method presented here is to integrate the gyroscopedata on one processor then the integrated data is sent to a differentprocessor to do the sensor fusion. When the integrated gyroscope data issent to the second processor, the integrated gyro data rate can be lessthan the original gyroscope data rate. The processor doing the gyroscopedata integration can be designed to take less power than the processordoing the sensor fusion. Sending the integrated gyroscope data at alower data rate than the data rate of the gyroscope data has similarperformance to sending the gyroscope data out at the original gyroscopedata rate. Running sensor fusion at the integrated gyroscope data ratehas similar performance to running sensor fusion at the gyroscope datarate. Lowering the sample rate reduces data traffic. Lowering the sensorfusion rate reduces computation. The gyroscope data is not required tobe sent out for the sensor fusion, which leads to further data trafficreductions. Approximate gyroscope data can be computed from theintegrated gyroscope data being sent out, by computing the derivate ofthe integrated gyroscope data. The estimate gyroscope data derived fromtaking the derivative of the integrated gyroscope data (LPQ) can be usedfor other algorithms such as determining the gyroscope data bias.Sending out integrated gyroscope data also allows a sensor with agyroscope to output this integrated gyroscope data as an output, andhave others write a sensor fusion algorithm using a reduced data rate.In one embodiment, the gyroscope integration will be done into aquaternion and can be done in software, firmware, or hardware on aseparate device from the sensor fusion. In an embodiment of theinvention, the gyroscope and the processor, performing the integrationare in separate packages. In yet another embodiment of the invention,the gyroscope and the processor, performing the gyroscope integration,are in the same package or substrate.

There are several methods of integrating gyroscope data. Given angularvelocity in radians/second in Equation 4 with magnitude ω_(m) shown inEquation 5.

ω=[ω_(x), ω_(y), ω_(x)]  Equation 4

ω_(m)=√{square root over(ω_(x)·ω_(x)+ω_(y)·ω_(y)+ω_(z)·ω_(z))}  Equation 5

a quaternion can be defined as shown in Equation 6.

$\begin{matrix}{\overset{\_}{Q_{\omega}} = \begin{bmatrix}{\cos \left( {\omega_{m} \cdot \frac{\Delta \; t}{2}} \right)} \\{\frac{\omega_{0}}{\omega_{m}} \cdot {\sin \left( {\omega_{m} \cdot \frac{\Delta \; t}{2}} \right)}} \\{\frac{\omega_{1}}{\omega_{m}} \cdot {\sin \left( {\omega_{m} \cdot \frac{\Delta \; t}{2}} \right)}} \\{\frac{\omega_{2}}{\omega_{m}} \cdot {\sin \left( {\omega_{m} \cdot \frac{\Delta \; t}{2}} \right)}}\end{bmatrix}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

Where Δt is time elapsed between time step M−1 and time step M. Tointegrate a quaternion using constant angular velocity from time stepM−1 to time step M, the formula in Equation 7 is used.

Q_(M) =Q_(M−1) ⊗Q_(ω)   Equation 7

There are other approximations that can be used to integrate quaterniondata such as:

$\begin{matrix}{{\overset{\_}{Q_{M}} = {\overset{\_}{Q_{M - 1}} + {\frac{t}{2} \cdot {\overset{\_}{Q_{M - 1}} \otimes \begin{bmatrix}0 \\\omega_{x} \\\omega_{y} \\\omega_{z}\end{bmatrix}}}}}{\overset{\_}{Q_{M}} = \frac{\overset{\_}{Q_{M}}}{\overset{\_}{Q_{M}}}}} & {{Equation}\mspace{14mu} 8}\end{matrix}$

In Equation 8, the previous quaternion is multiplied by the rotationrate from the gyroscope (in radians) using a quaternion multiply. Thisis scaled by the time between samples over 2 and added to the previousquaternion. Next, the quaternion is divided by the magnitude to maintaina magnitude of 1. Note, optimizations that may be used would be to useraw gyroscope units and to scale by a constant that encompasses both theconversion to radians and the sample rate over 2. Other optimizationsfor the normalization to 1, would be to use a Taylor series to avoid theinverse square root. Furthermore, this could be converted to fixed pointor integer math by one skilled in the art.

The gyroscope data typically has noise on it. Integrating noise on thegyroscope data will cause the quaternion to drift. To overcome thisdrift in a stationary position, the gyroscope data could be set to zeroif the absolute value of the gyroscope data is below a threshold.

If the gyroscope bias is known, the gyroscope bias could be subtractedout of the gyroscope data before integrating the gyroscope data. Thiscan be done in software, firmware or hardware.

With each sample of quaternion data that is received, independent of therate at which the angular velocity was integrated at, an angularvelocity bias can be removed even after the bias was integrated into thesolution. A quaternion going from one time step to another can bemodeled as Equation 9 where Q_(A) is the adjustment between time steps Nand N−1. Q_(A) is independent upon the mechanism of going from Q_(N) toQ_(N−1) . This is similar to Equation 7 and

$\overset{\_}{Q_{M}} = \frac{\overset{\_}{Q_{M}}}{\overset{\_}{Q_{M}}}$

Equation 8 going from Q_(M) to Q_(M−1) with a constant angular rate.

Q_(N) =Q_(N−1) ⊗Q_(A)   Equation 9

The next step is to maintain a unit quaternion variable with thegyroscope bias removed after the gyroscope bias has been integrated intothe quaternion. After combining these ideas, Equation 10 shows how toremove a constant bias.

Q_(BR,N) =Q_(BR,N−1) ⊗Q_(M−1) ′⊗Q_(M) ⊗Q_(B) ′  Equation 10

Where Q_(B) ′ is computed by plugging the gyroscope bias into Equation 6and taking the inverse as shown in Equation 3. Q_(BR,N) is thequaternion with the gyroscope bias removed. To use the integratedgyroscope quaternion with another correction, a transformation betweenthe quaternion result for sensor fusion and the integrated gyroscopequaternion as shown in Equation 10. The sensor fusion quaternion at timeN, is shown as Q_(F,N) in Equation 11 where Q_(C) is the correction.

Q_(F,N) =Q_(C) ⊗Q_(BR,N)   Equation 11

After doing sensor fusion and updating Q_(F,N) , to the new time step,Q_(F,N+1) , a new conversion (Q_(C) ) from the integrated gyroscopequaternion is computed and saved.

Q_(C) =Q_(F,N+1) ⊗Q_(BR,N) ′  Equation 12

If the bias correction is done before integrated the gyroscope data intoa quaternion, Q_(B) would be identity and could be simplified or ignoredin all the equations presented. The equations would stay the same.

The basic idea presented so far has the quaternion integration startingfrom zero and sending out the quaternion referenced from that point.Instead, the starting point could be reset at each time output step soonly the change from one time step to the next is sent out. This willsimplify some of the equations. This change can be represented as Q_(A)in Equation 9. This would simplify Equation 10 into Equation 13 asfollows:

Q_(BR,N) =Q_(BR,N−1) ⊗Q_(A) ⊗Q_(B) ′  Equation 13

Further optimization would allow the quaternion integration change withbias correction to be applied directly to the sensor fusion quaternion.

Q_(F,N) =Q_(F,N−1) ⊗Q_(A) ⊗Q_(B) ′  Equation 14

Similarly, the Q_(B) term could be simplified or ignored in bothEquation 13 and Equation 14 as mentioned above, if the bias correctionis done before integrating.

Noted, that while outputting the integration over one time step yieldssimpler equations, it is less immune to dropping samples. Often, if asystem gets overloaded, samples could get lost, if the integration isdone cumulatively across many samples, losing a sample would causes lesserror.

To further reduce the data traffic, it can be noted, that a quaternionhas magnitude one, doing so would allow 3 components to be transferredacross a data line, and to have the fourth element reconstructed. Whentrying to reconstruct the fourth element, there can be some signambiguity. To overcome the sign ambiguity, it is possible to force thesign of the missing component to have a known value. This is because inquaternion space,Q=−Q. For example, suppose, you want to send the last 3elements and reconstruct the first element, then if the first element isnegative, simply negate all the terms being sent across the data line.Then when reconstructing the first element, it would be known, that thatelement is always positive.

Other embodiments would include but not limited to other ways to passinformation equivalent to quaternion data. As a quaternion represents anorientation in space, an orientation matrix or Euler angles could besubstituted for the quaternion.

There are various ways to integrate a quaternion. Below is a descriptionon quaternion integration along with new methods.

Zero'th Order Quaternion Integration

Integrating a quaternion is equivalent to solving the correspondingfirst order ordinary differential equation:

{dot over (Q)}(t)=½Ω(ω)Q(t)  Equation 15

Where

${{\Omega (\omega)} = \begin{bmatrix}0 & {- \omega_{x}} & {- \omega_{y}} & {- \omega_{z}} \\\omega_{x} & 0 & \omega_{z} & \omega_{x} \\\omega_{y} & {- \omega_{z}} & 0 & {- \omega_{y}} \\\omega_{z} & \omega_{y} & {- \omega_{x}} & 0\end{bmatrix}},$

is a skew symmetric matrix. Assuming the angular velocity vector ω isconstant between time t and t+Δt. The generalized solution to thisdifferential equation is

Q (t+Δt)=e ^(1/2)Ω(ω)Δt Q (t)  Equation 16

In order to calculate the above solution, the matrix exponent of theskew-symmetric matrix Ω has to be calculated. Calculating the exponentof a matrix numerically is very expensive. In real-time implementations,a simplified approximation is used, with the procedure:

Δ Q ≅{dot over (Q)}(t)⊗Δt  Equation 17

Where (1) is used with a Forward Euler approximation, and

$\begin{matrix}{{\overset{\_}{Q}\left( {t + {\Delta \; t}} \right)} = {{\overset{\_}{Q}(t)} + {\Delta \overset{\_}{Q}}}} & {{Equation}\mspace{14mu} 18} \\{{\overset{\_}{Q}\left( {t + {\Delta \; t}} \right)} = \frac{\overset{\_}{Q}\left( {t + {\Delta \; t}} \right)}{{\overset{\_}{Q}\left( {t + {\Delta \; t}} \right)}}} & {{Equation}\mspace{14mu} 19}\end{matrix}$

In this approximation, the change in the quaternion is not computed inthe rotation space, i.e. Equation 18 violates the rules of quaternionalgebra. This causes small errors in the quaternion calculation, whichcan grow over time. In order to maintain an accurate quaternion withunit norm, the quaternion is re-normalized, as shown in the final stepEquation 19.

An alternative method to compute Equation 16 more accurately is asfollows. First, the matrix e^(1/2Ω(ω)Δt) is expanded in a Taylor Seriesas follows:

$\begin{matrix}{{e^{\frac{1}{2}{\Omega {(\omega)}}\Delta \; t} = {I_{4 \times 4} + {\frac{1}{2}{\Omega (\omega)}\Delta \; t} + {\frac{1}{2!}\left( {\frac{1}{2}\Omega \; (\omega)\Delta \; t} \right)^{2}} + {\frac{1}{3!}\left( {\frac{1}{2}\Omega \; (\omega)\Delta \; t} \right)^{3}} + \; {.\;.\;.}}}\mspace{20mu}} & {{Equation}\mspace{14mu} 20}\end{matrix}$

Using the properties of the skew-symmetric matrix Ω(ω) and the Eulerformula, as discussed in the reference “Matrix Mathematics: Theory,Facts, and Formulas”, Princeton University Press, Second Edition, 2009,by Dennis S. Bernstein, p. 741, exponents of skew-symmetric matrices canbe rewritten as:

$\begin{matrix}{e^{\frac{1}{2}{\Omega {(\omega)}}\Delta \; t} = {{{\cos \left( {\frac{\omega_{m}}{2}\Delta \; t} \right)}I_{4 \times 4}} + {\frac{1}{\omega_{m}}{\sin \left( {\frac{\omega_{m}}{2}\Delta \; t} \right)}{\Omega (\omega)}}}} & {{Equation}\mspace{14mu} 21}\end{matrix}$

Where ω_(m) is the magnitude of the angular rate ω, as defined in

ω_(m)=√{square root over (ω_(x)·ω_(x)+ω_(y)·ω_(y)+ω_(z)·ωhdz)}  Equation 5

Then,

$\begin{matrix}{{\overset{\_}{Q}\left( {t + {\Delta \; t}} \right)} = {\left( {{{\cos \left( {\frac{\omega_{m}}{2}\Delta \; t} \right)}I_{4 \times 4}} + {\frac{1}{\omega_{m}}{\sin \left( {\frac{\omega_{m}}{2}\Delta \; t} \right)}{\Omega (\omega)}}} \right){\overset{\_}{Q}(t)}}} & {{Equation}\mspace{14mu} 22}\end{matrix}$

Close inspection of Eqn. Equation 22 reveals that this matrix-vectormultiplication is equivalent to a quaternion multiplication:

$\begin{matrix}{{\overset{\_}{Q}\left( {t + {\Delta \; t}} \right)} = {{\begin{bmatrix}{\cos \left( {\frac{\omega_{m}}{2}\Delta \; t} \right)} \\{\frac{\omega}{\omega_{m}}{\sin \left( {\frac{\omega_{m}}{2}\Delta \; t} \right)}}\end{bmatrix} \otimes {\overset{\_}{Q}(t)}} = {\Delta \; {\overset{\_}{Q} \otimes {\overset{\_}{Q}(t)}}}}} & {{Equation}\mspace{14mu} 23}\end{matrix}$

Where

${\Delta \overset{\_}{Q}} = \begin{bmatrix}{\cos \left( {\frac{\omega_{m}}{2}\Delta \; t} \right)} \\{\frac{\omega}{\omega_{m}}{\sin \left( {\frac{\omega_{m}}{2}\Delta \; t} \right)}}\end{bmatrix}$

is the 4×1 quaternion that represents the quaternion rotation induced bythe angular velocity ω over time span Δt. Comparing this with Equation18, it is noted that the formula in Equation 23 is exact, and itpreserves the quaternion norm, i.e. no re-normalization of thequaternion is necessary.

Note that for very small angular velocity ω, the above expression cancause numerical instability. In this case, a first order approximationof Equation 21 can be used:

$\begin{matrix}{{\lim\limits_{\omega\rightarrow 0}e^{\frac{1}{2}{\Omega {(\omega)}}\Delta \; t}} = {I_{4 \times 4} + {\frac{1}{2}{\Omega (\omega)}\Delta \; t}}} & {{Equation}\mspace{14mu} 24}\end{matrix}$

First Order Quaternion Integration

The zero'th order quaternion integration assumes that the angularvelocity ω is constant over the period Δt. A better and more accurateintegration can be derived if the angular velocity is assumed to beevolving linearly over the time Δt. For this purpose, ω(t_(k)) isintroduced for the value of velocity ω at time t_(k), and ω(t_(k+1)) forω at time t_(k+1), with Δt=t_(k+1)−t_(k).

The average angular velocity over the time Δt is:

ω=½(ω(t _(k+1))+ω(t _(k)))  Equation 25

Also, define the derivative of the angular velocity as {dot over (ω)}.The corresponding matrix Ω({dot over (ω)}), in the linear case, isconstant:

$\begin{matrix}{{\Omega \left( \overset{.}{\omega} \right)} = {\Omega \left( \frac{{\omega \left( t_{k + 1} \right)} - {\omega \left( t_{k} \right)}}{\Delta \; t} \right)}} & {{Equation}\mspace{14mu} 26}\end{matrix}$

And the higher order derivatives are zero due to the assumption oflinearly changing angular velocity ω. In the following, Ω(ω) isrepresented as Ω, and Ω({dot over (ω)}) as {dot over (Ω)} forconciseness.

As described in the publication “Spacecraft Attitude Determination andControl”, edited by J. R. Wertz, D. Reidel Publishing Co., Dordrecht,The Netherlands, 1978, p. 565, Eqn. (17-21)] in order to derive thehigher order quaternion derivative expression, the Taylor seriesexpansion of the quaternion is done at time instant t_(k+1) in terms ofthe quaternion and its derivatives at time t_(k) is as follows:

$\begin{matrix}{{\overset{\_}{Q}\left( t_{k + 1} \right)} = {{\left\lbrack {I_{4 \times 4} + {\frac{1}{2}\Omega \; \Delta \; t} + {\frac{1}{2!}\left( {\frac{1}{2}\Omega \; \Delta \; t} \right)^{2}} + {\frac{1}{3!}\left( {\frac{1}{2}\Omega \; \Delta \; t} \right)^{3}} + \; {.\;.\;.}}\mspace{20mu} \right\rbrack {\overset{\_}{Q}\left( t_{k} \right)}} + {\frac{1}{4}\overset{.}{\Omega}\; \Delta \; t^{2}{\overset{\_}{Q}\left( t_{k} \right)}} + {\left\lbrack {{\frac{1}{12}\overset{.}{\Omega}\Omega} + {\frac{1}{24}\Omega \overset{.}{\Omega}}} \right\rbrack {\overset{\_}{Q}\left( t_{k} \right)}} + \; {.\;.\;.}}} & {{Equation}\mspace{14mu} 27}\end{matrix}$

Using the fact that

$\begin{matrix}{{\Omega \left( \overset{.}{\omega} \right)} = {{\Omega \left( \frac{{\omega \left( t_{k + 1} \right)} - {\omega \left( t_{k} \right)}}{\Delta \; t} \right)} = {\frac{1}{\Delta \; t}\left\lbrack {{\Omega \left( {\omega \left( t_{k + 1} \right)} \right)} - {\Omega \left( {\omega \left( t_{k} \right)} \right)}} \right\rbrack}}} & {{Equation}\mspace{14mu} 28}\end{matrix}$

Ω(ω) can be written as:

Ω(ω)=Ω=½[Ω(ω(t _(k+1)))+Ω(ω(t _(k)))]=Ω(ω(t _(k)))+½Ω({dot over (ω)}(t_(k)))Δt=Ω+½{dot over (Ω)}Δt  Equation 29

Using Equation 29 the terms in Equation 27 can be rearranged as follows(Eqn. (17-23 in Wertz)):

$\begin{matrix}{{\overset{\_}{Q}\left( t_{k + 1} \right)} = {{\left\lbrack {I_{4 \times 4} + {\frac{1}{2}\overset{\_}{\Omega}\; \Delta \; t} + {\frac{1}{2!}\left( {\frac{1}{2}\overset{\_}{\Omega}\; \Delta \; t} \right)^{2}} + {\frac{1}{3!}\left( {\frac{1}{2}\overset{\_}{\Omega}\; \Delta \; t} \right)^{3}} + \; {.\;.\;.}}\mspace{20mu} \right\rbrack {\overset{\_}{Q}\left( t_{k} \right)}} + {{\frac{1}{48}\left\lbrack {\left( {{\overset{.}{\Omega}\Omega} + {\Omega \overset{.}{\Omega}}} \right)\Delta \; t^{3}} \right\rbrack}{\overset{\_}{Q}\left( t_{k} \right)}}}} & {{Equation}\mspace{14mu} 30}\end{matrix}$

The first of the two terms on the right-hand side of Equation 30 issimilar to the Taylor Series expansion of Equation 16, and thus has thesame form. Note that this differs from Equation 16 in usingtime-averaged rate information rather than instantaneous rates. Thesecond term with the derivatives of the angular rate can be expandedusing Equation 26. This gives:

Q(t _(k+1))=[e ^(1/2Ω(ω)Δt)+ 1/48(Ω(ω(t _(k+1)))Ω(ω(t _(k)))−Ω(ω(t_(k)))Ω(ω(t _(k+1))))Δt ²] Q (t _(k))Equation 31

In order to simplify the expression in Equation 30, the following isnoted.

First, form the cross product of the angular velocities ω(t_(k)) andω(t_(k+1)):

ω₆₆=ω(t _(k))×ω(t _(k+1))  Equation 32

For the second expression in Equation 31 the following holds:

Ω(ω₆₆)=2Ω(ω(t _(k+1)))Ω(ω(t _(k)))−Ω(ω(t _(k)))Ω(ω(t _(k+1)))  Equation33

i.e. the skew-symmetric form of ω₆₆ is the same as the right hand sideof Equation 33. This can be verified by expanding the terms andcomparing the elements. Introducing Equation 32 and Equation 33 intoEquation 31, the following is derived:

Q (t _(k+1))=[e ^(1/2Ω(ω)Δt)+ 1/24Ω(ω₆₆)Δt ²] Q (t _(k))  Equation 34

It is noted that the second term of Equation 34, can be rewritten as:

$\begin{matrix}{{{\Omega \left( \omega_{\Delta} \right)}\mspace{11mu} {\overset{\_}{Q}\left( t_{k} \right)}} = {{\begin{bmatrix}0 \\\omega_{\Delta \; x} \\\omega_{\Delta \; y} \\\omega_{\Delta \; z}\end{bmatrix} \otimes {\overset{\_}{Q}\left( t_{k} \right)}} = {{\overset{\_}{Q}\left( \omega_{\Delta} \right)} \otimes {\overset{\_}{Q}\left( t_{k} \right)}}}} & {{Equation}\mspace{14mu} 35}\end{matrix}$

Comparing the above with Equation 20-Equation 23, the following can bederived:

Q (t _(k+1))=Δ Q ⊗q(t _(k))+ 1/24Δt ² Q (ω₆₆)⊗Q(t _(k))  Equation 36

Where

${\Delta \overset{\_}{Q}} = \begin{bmatrix}{\cos \left( {\frac{\overset{\_}{\omega_{m}}}{2}\Delta \; t} \right)} \\{\frac{\overset{\_}{\omega}}{\overset{\_}{\omega_{m}}}{\sin \left( {\frac{\overset{\_}{\omega_{m}}}{2}\Delta \; t} \right)}}\end{bmatrix}$

is the 4×1 quaternion that represents the quaternion rotation induced bythe average angular velocity ω over time span Δt. Next, simplifying theexpression results in the following:

Q (t _(k+1))=(Δ Q + 1/24Δt ² Q (ω₆₆))⊗ Q (t _(k))=Δ Q (ω, ω₆₆)⊗ Q (t_(k))  Equation 37

Note that the term (ΔQ+ 1/24Δt² Q(ω₆₆)) needs to be normalized.

To summarize, the computational steps for the first order quaternionintegration are as follows:

$\mspace{20mu} {{{Calculate}\mspace{20mu} \overset{\_}{\omega}} = {\frac{1}{2}\left( {{\omega \left( t_{k + 1} \right)} + {\omega \left( t_{k} \right)}} \right)}}$$\mspace{20mu} {{{Calculate}\mspace{14mu} {quaternion}\mspace{14mu} \Delta \overset{\_}{Q}} = \begin{bmatrix}{\cos \left( {\frac{\overset{\_}{\omega_{m}}}{2}\Delta \; t} \right)} \\{\frac{\overset{\_}{\omega}}{\overset{\_}{\omega_{m}}}{\sin \left( {\frac{\overset{\_}{\omega_{m}}}{2}\Delta \; t} \right)}}\end{bmatrix}}$$\mspace{20mu} {{{{Calculate}\mspace{14mu} \omega_{\Delta}} = {{\omega \left( t_{k} \right)} \times {\omega \left( t_{k + 1} \right)}}},{{{and}\mspace{14mu} {\overset{\_}{Q}\left( \omega_{\Delta} \right)}} = \begin{bmatrix}0 \\\omega_{\Delta \; x} \\\omega_{\Delta \; y} \\\omega_{\Delta \; z}\end{bmatrix}}}$${{Calculate}\mspace{14mu} {quaternion}\mspace{14mu} \Delta {\overset{\_}{Q}\left( {\overset{\_}{\omega},\omega_{\Delta}} \right)}} = {{\Delta \; \overset{\_}{Q}} + {\frac{1}{24}\Delta \; t^{2}{\overset{\_}{Q}\left( \omega_{\Delta} \right)}\mspace{14mu} {and}\mspace{14mu} {normalize}\mspace{14mu} \Delta \; {Q\left( {\overset{\_}{\omega},\omega_{\Delta}} \right)}}}$$\mspace{20mu} {{{Calculate}\mspace{14mu} {\overset{\_}{Q}\left( t_{k + 1} \right)}} = {\Delta {{\overset{\_}{Q}\left( {\overset{\_}{\omega},\omega_{\Delta}} \right)} \otimes {{\overset{\_}{Q}\left( t_{k} \right)}.}}}}$

A computationally simpler version of the first order quaternionintegration is as follows:

Given angular rates ω(t−Δt), ω(t), at time instants t and t−Δt, and thecurrent quaternion Q(t), define ω as in Equation 25, and ω_(Δ) as inEquation 32.

${{{Let}\mspace{14mu} {\overset{\_}{Q}}_{\overset{\_}{\omega}}} = \begin{bmatrix}0 \\{\overset{\_}{\omega}}_{x} \\{\overset{\_}{\omega}}_{y} \\{\overset{\_}{\omega}}_{z}\end{bmatrix}},{{{and}\mspace{20mu} {\overset{\_}{Q}}_{\omega_{\Delta}}} = {\begin{bmatrix}0 \\\omega_{\Delta \; x} \\\omega_{\Delta \; y} \\\omega_{\Delta \; z}\end{bmatrix}.}}$

Then, compute:

Δ Q = Q (t)⊗(½Δt* Q _(ω) + 1/24Δt ²* Q ₁₀₇ ₆₆ )  Equation 38

and, as in Equation 39 the quaternion is computed and normalized with:

${\overset{\_}{Q}\left( {t + {\Delta \; t}} \right)} = {{\overset{\_}{Q}(t)} + {\Delta \; \overset{\_}{Q}}}$${\overset{\_}{Q}\left( {t + {\Delta \; t}} \right)} = \frac{\overset{\_}{Q}\left( {t + {\Delta \; t}} \right)}{{\overset{\_}{Q}\left( {t + {\Delta \; t}} \right)}}$

Although the description has been described with respect to particularembodiments thereof, these particular embodiments are merelyillustrative, and not restrictive.

As used in the description herein and throughout the claims that follow,“a”, “an”, and “the” includes plural references unless the contextclearly dictates otherwise. Also, as used in the description herein andthroughout the claims that follow, the meaning of “in” includes “in” and“on” unless the context clearly dictates otherwise.

Thus, while particular embodiments have been described herein, latitudesof modification, various changes, and substitutions are intended in theforegoing disclosures, and it will be appreciated that in some instancessome features of particular embodiments will be employed without acorresponding use of other features without departing from the scope andspirit as set forth. Therefore, many modifications may be made to adapta particular situation or material to the essential scope and spirit.

What we claim is:
 1. A system comprising; a first silicon substrateincluding a first processor and configured to receive 3-axis data, the3-axis data indicative of an orientation of a device, the 3-axis dataincluding a sensor bias, wherein the first processor is configured toremove the sensor bias and generate a first processor output, furtherwherein, the first processor is configured to implement a low powerquaternion algorithm on the first processor output, the low powerquaternion algorithm causing integration of at least a portion of thefirst processor data and generate integration data and the integrationdata is a representation of quaternion data, the quaternion datarepresentative of a quaternion operation performed on the integrationdata; a second silicon substrate communicatively coupled to the firstsilicon substrate and configured to receive the quaternion data, thesecond silicon substrate configured to execute sensor fusion operationon the quaternion data and generate sensor fusion data, wherein thequaternion data has a first data rate and the sensor fusion data has asecond data rate, wherein the second data rate is lower than the firstdata rate, the sensor fusion data transmitted to external devicesrequiring lower data rate input.